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Abstract:

This invention is directed to appropriately searching for case data
similar in the process of a disease. A similar case search apparatus
(100) according to this invention includes a disease progress model
building function of building a disease progress model by extracting
feature amounts from a plurality of medical images obtained by imaging
the same object in different periods, a unit configured to read out case
data, a unit configured to acquire inspection data, a similar case search
function of interpolating, by using the model, feature amounts extracted
from the plurality of medical images contained in the inspection data,
and calculate a similarity between the inspection data and the case data
by using the interpolated feature amounts, and a monitor (111) which
displays case data selected based on the calculated similarity.

Claims:

1. An information processing apparatus comprising: an interpolation unit
configured to interpolate, by using a model, at least either of feature
amounts extracted from a plurality of medical images contained in case
data and feature amounts extracted from a plurality of medical images
contained in inspection data; a calculation unit configured to calculate
similarities between the plurality of medical images contained in the
inspection data and the plurality of medical images contained in the case
data by using the feature amounts interpolated by the interpolation unit;
and an output unit configured to output case data selected based on the
calculated similarities.

2. The information processing apparatus according to claim 1, further
comprising a building unit configured to build a temporal feature amount
change model by extracting feature amounts from a plurality of medical
images obtained by imaging the same object in different periods, wherein
the model to be used by the interpolation unit is built by the building
unit.

3. The information processing apparatus according to claim 1, further
comprising a database which stores case data containing a plurality of
medical images obtained by imaging the same object in different periods,
wherein the case data to be used by the interpolation unit is read out
from the database.

4. The information processing apparatus according to claim 1, wherein the
interpolation unit interpolates the feature amounts by using a model
selected based on at least one of a nature of a disease of an object, a
sex of the object, an age of the object, and a treatment method of the
disease.

5. The information processing apparatus according to claim 1, wherein the
interpolation unit classifies the plurality of medical images into
medical images obtained before treatment and medical images obtained
after treatment, and interpolates feature amounts extracted from the
respective medical images.

6. The information processing apparatus according to claim 5, wherein the
calculation unit calculates similarities between the plurality of medical
images contained in the inspection data and the plurality of medical
images contained in the case data separately for the medical images
obtained before treatment and the medical images obtained after
treatment.

7. The information processing apparatus according to claim 5, wherein the
calculation unit comprises: a first calculation unit configured to
calculate a similarity between the inspection data containing the medical
images obtained before treatment and the case data containing the medical
images obtained before treatment, and a second calculation unit
configured to calculate a similarity between the inspection data
containing the medical images obtained after treatment and the case data
containing the medical images obtained after treatment.

8. The information processing apparatus according to claim 7, wherein the
output unit outputs case data selected based on a total similarity
calculated using the similarity calculated by the first calculation unit
and the similarity calculated by the second calculation unit.

9. The information processing apparatus according to claim 7, wherein the
output unit comprises: a first output unit configured to output case data
selected based on the similarity calculated by the first calculation
unit, and a second output unit configured to output case data selected
based on the similarity calculated by the second calculation unit.

10. An information processing method in an information processing
apparatus, comprising: interpolating, by using a model, at least either
of feature amounts extracted from a plurality of medical images contained
in case data and feature amounts extracted from a plurality of medical
images contained in inspection data; calculating similarities between the
plurality of medical images contained in the inspection data and the
plurality of medical images contained in the case data by using the
interpolated feature amounts; and outputting case data selected based on
the calculated similarities.

11. A computer-readable storage medium storing a program for causing a
computer to execute an information processing method comprising:
interpolating, by using a model, at least either of feature amounts
extracted from a plurality of medical images contained in case data and
feature amounts extracted from a plurality of medical images contained in
inspection data; calculating similarities between the plurality of
medical images contained in the inspection data and the plurality of
medical images contained in the case data by using the interpolated
feature amounts; and outputting case data selected based on the
calculated similarities.

Description:

TECHNICAL FIELD

[0001] The present invention relates to an information processing
technique for searching for similar case data.

BACKGROUND ART

[0002] Medical documents and medical images are becoming digital along
with recent popularization of medical information systems including HIS
and PACS. HIS stands for a hospital information system, and PACS stands
for a picture archiving and communication system.

[0003] Medical images (e.g., X-ray image, CT image, and MRI image), which
were often viewed on a film viewer after developed on films, are
digitized now and can be viewed on a monitor.

[0004] More specifically, digital medical images (medical image data) can
be stored in the PACS or the like, and if necessary, read out from it and
interpreted on the monitor of an image interpretation terminal.

[0005] Medical documents such as a medical record are also being digitized
as medical record data. Medical record data of a patient serving as an
object can be read out from the HIS or the like and viewed on the monitor
of an image interpretation terminal.

[0006] In the digital environment, an image interpreter can receive an
image interpretation request form by a digital message. Based on the
message, he reads out medical image data of a patient from the PACS and
makes a diagnosis while displaying it on the image interpretation monitor
of an image interpretation terminal. If necessary, the image interpreter
reads out medical record data of the patient from the HIS and makes a
diagnosis while displaying it on another monitor.

[0007] A desire to reduce the burden on an image interpreter in image
interpretation has urged the development of medical image processing
apparatuses. This apparatus makes a computer-aided diagnosis by analyzing
medical image data to automatically detect a morbid portion or the like.
Computer-aided diagnosis will be referred to as CAD.

[0008] CAD can automatically detect an abnormal shadow candidate as a
morbid portion and display it. More specifically, a computer can process
medical image data such as an X-ray image to detect and display an
abnormal tumor shadow or high-density small calcified shadow caused by a
cancer or the like. The use of CAD can reduce the burden on an image
interpreter in image interpretation and increase the image interpretation
accuracy.

[0009] Another technique for reducing the burden on an image interpreter
in image interpretation is disclosed in, for example, patent reference 1
listed below. The technique in patent reference 1 can automatically
detect an abnormal candidate from medical image data and automatically
set a region (to be referred to as a region of interest) containing the
abnormal candidate portion. This technique can save an image interpreter
from having to manually set a region of interest.

[0010] Demand has also arisen for developing a technique for further
increasing the image interpretation accuracy by an image interpreter in
image interpretation. Generally when interpreting medical image data to
make a diagnosis, an image interpreter sometimes hesitates to decide a
diagnosis name if a morbid portion in the medical image data during
interpretation has an unfamiliar image feature or there are a plurality
of morbid portions having similar image features.

[0011] In this case, the image interpreter at a loss may ask advice for
another experienced image interpreter, or refer to documents such as
medical books and read the description of an image feature regarding a
suspicious disease name. Alternatively, he may examine illustrated
medical documents to locate a photo similar to a morbid portion captured
in the medical image data during image interpretation, and read a disease
name corresponding to the photo for reference of the diagnosis.

[0012] However, the image interpreter may not always have an advisory
experienced image interpreter. Even if the image interpreter examines
medical documents, he may not be able to locate a photo similar to a
morbid portion captured in the medical image data during image
interpretation, or the description of an image feature.

[0013] To solve this problem by a digital means and increase the image
interpretation accuracy, similar case search apparatuses have been
developed recently. The basic idea of the similar case search apparatus
is to support a diagnosis by searching for a plurality of case data from
those accumulated in the past based on any criterion and presenting them
to an image interpreter.

[0014] As a general method in similar case search, it is known to search
an image database accumulated in the past for image data similar in image
feature amount to medical image data during image interpretation.

[0015] Diagnosis is sometimes made based on the result of follow-up such
as the progress of a disease. In this case, the similarity of medical
image data at one time point is determined. Also, the similarity of the
process is determined based on a plurality of medical image data obtained
by imaging the same patient in different periods. By presenting case data
similar in process, this method can present highly reliable reference
information in disease diagnosing, subsequent inspection planning,
treatment planning, and the like.

[0016] For example, patent reference 2 listed below discloses a similar
case search method which assists a diagnosis by determining the
similarity of the process based on a plurality of medical image data
obtained by imaging the same patient in different periods.

[0017] According to patent reference 2, case data which are similar in
image feature amount to respective time-series medical image data to be
inspected and are equal in imaging time interval to them can be presented
as case data similar in the process of a disease.

[0020] However, according to the invention disclosed in patent reference
2, only case data equal in imaging time interval to time-series medical
image data to be inspected can be presented as a case data candidate
similar in process.

[0021] In other words, case data different in imaging time interval from
time-series medical image data to be inspected cannot be presented as
similar case data even if the actual process is similar.

[0022] To increase the image interpretation accuracy, it is desirable to
appropriately search for case data similar in process regardless of the
imaging time interval.

[0023] The present invention has been made to overcome the conventional
problems.

SUMMARY OF THE INVENTION

[0024] An information processing apparatus according to the present
invention has the following arrangement. That is, an information
processing apparatus comprises

[0025] a building unit configured to build a temporal feature amount
change model by extracting feature amounts from a plurality of medical
images obtained by imaging the same object in different periods,

[0026] a readout unit configured to read out, from a database, case data
containing a plurality of medical images obtained by imaging the same
object in different periods,

[0027] an acquisition unit configured to acquire inspection data
containing a plurality of medical images obtained by imaging an object to
be inspected in different periods,

[0028] an interpolation unit configured to interpolate, by using the
model, either of feature amounts extracted from the plurality of medical
images contained in the case data and feature amounts extracted from the
plurality of medical images contained in the inspection data,

[0029] a calculation unit configured to calculate similarities between the
plurality of medical images contained in the inspection data and the
plurality of medical images contained in the case data by using the
feature amounts interpolated by the interpolation unit, and

[0030] an output unit configured to output case data selected based on the
calculated similarities.

[0031] The present invention can appropriately search for case data
similar in process.

[0032] Other features and advantages of the present invention will become
apparent from the following description of exemplary embodiments with
reference to the accompanying drawings. Note that the same reference
numerals denote the same or similar parts throughout the accompanying
drawings.

BRIEF DESCRIPTION OF DRAWINGS

[0033] FIG. 1 is a block diagram showing the overall configuration of a
medical information system having a similar case search apparatus
(information processing apparatus) and the arrangement of the similar
case search apparatus according to the first embodiment of the present
invention;

[0035] FIG. 3 is a flowchart showing the sequence of disease progress
model building processing;

[0036] FIG. 4 is a graph of discrete time-series data;

[0037] FIG. 5 is a graph showing a disease progress model;

[0038] FIG. 6 is a flowchart showing the sequence of similar case search
processing;

[0039] FIG. 7 is a graph of discrete time-series data;

[0040] FIG. 8 is a graph of interpolated discrete time-series data;

[0041] FIG. 9A is a view exemplifying a similar case data presentation
method;

[0042] FIG. 9B is a view exemplifying the similar case data presentation
method;

[0043] FIG. 10 is a flowchart showing the sequence of similar case search
processing;

[0044] FIG. 11A is a view exemplifying a similar case data presentation
method; and

[0045] FIG. 11B is a view exemplifying the similar case data presentation
method.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] Preferred embodiments of the present invention will now be
described with reference to the drawings.

First Embodiment

1. Overall Configuration of Medical Information System and Arrangement of
Similar Case Search Apparatus

[0047] FIG. 1 is a block diagram showing the overall configuration of a
medical information system having a similar case search apparatus
(information processing apparatus) and the arrangement of the similar
case search apparatus according to the first embodiment of the present
invention.

[0049] The controller 110 includes a central processing unit (CPU) 101,
main memory 102, magnetic disk 103, display memory 104, and common bus
105. The CPU 101 executes control programs stored in the main memory 102
to achieve various control operations such as communication with a case
database 120, medical image database 130, and medical record database 140
and control of the overall similar case search apparatus 100.

[0050] The CPU 101 executes a control program for controlling the
operation of each building component of the similar case search apparatus
100, and a similar case search program which is the main function of the
apparatus. The main memory 102 stores control programs to be executed by
the CPU 101, and provides a work area when the CPU 101 executes the
similar case search program.

[0051] The magnetic disk 103 stores control programs such as an operating
system (OS) and device drivers for peripheral devices. In addition, the
magnetic disk 103 stores the similar case search program for implementing
a case data generation function, disease progress model building
function, and similar case search function (to be described later). The
magnetic disk 103 further stores various kinds of data used by the
similar case search program.

[0052] The display memory 104 temporarily stores display data for the
monitor 111. The monitor 111 is, for example, a CRT monitor or liquid
crystal monitor, and displays an image based on display data output from
the display memory 104.

[0053] The mouse 112 and keyboard 113 are building components used when
the user performs pointing input or inputs text or the like. The common
bus 105 connects these building components so that they can communicate
with each other.

[0054] Note that the configuration of the medical information system is
not limited to the configuration example of FIG. 1. For example, an
existing PACS is usable as the medical image database 130. An electronic
medical record system, which is a subsystem of an existing HIS, is
available as the medical record database 140.

[0055] It is also possible to connect external storage devices, for
example, an FDD, HDD, CD drive, DVD drive, MO drive, and ZIP drive to the
similar case search apparatus 100 and read case data, medical image data,
and medical record data from these drives.

[0056] By building the medical information system, the similar case search
apparatus 100 can read out case data from the case database 120 via a LAN
150. Also, the similar case search apparatus 100 can read out medical
image data from the medical image database 130 and medical record data
from the medical record database 140.

[0058] "Medical record data" stored in the medical record database 140
describes personal information (e.g., name, birth date, age, and sex) and
clinical information (e.g., various test values, chief complaint, past
history, and treatment history) of a patient serving as an object. The
"medical record data" further includes reference information to patient's
medical image data stored in the medical image database 130, and finding
information of a doctor in charge. After making a diagnosis, the medical
record data also includes a definite diagnosis name.

[0059] "Case data" stored in the case database 120 is information which
associates medical image data obtained by imaging the same patient in
different periods, corresponding medical record data, and data obtained
by analyzing time-series medical image data.

[0060] The case data generation function in the similar case search
program of the similar case search apparatus 100 is executed to generate
case data and store it in the case database 120. More specifically, case
data is generated as a copy of some of medical image data archived in the
medical image database 130 and medical record data archived in the
medical record database 140, or as link information to these data. The
generated case data is then stored.

[0061] Table 1 exemplifies a case data table stored in the case database
120. The case data table is a set of case data which are formed from the
same items and arranged regularly. In Table 1, medical image data of the
same patient are associated by the same case data ID (equivalent to a
patient ID).

[0062] The similar case search apparatus 100 according to the first
embodiment generates case data and then executes the similar case search
function.

[0063] More specifically, the similar case search apparatus 100 searches
the case database 120 for case data similar in process by using, as a
query, information (to be referred to as a case to be inspected) which is
a combination of medical image data (to be referred to as an image to be
inspected) of a patient to be inspected and past time-series medical
image data of the patient.

[0064] At this time, the similarity between a case to be inspected and
case data having a different imaging time interval can be calculated by
interpolating time-series data (to be referred to as discrete time-series
data) which are discrete for the image feature amount of the case to be
inspected. The method of interpolating discrete time-series data of a
case to be inspected can use, for example, other medical image data which
are partially equal in imaging time interval to a case to be inspected
and have similar medical image data.

[0065] However, it is difficult to interpolate the image feature amount of
a case to be inspected and generate successive time-series data by using
medical image data contained in case data. According to the first
embodiment, therefore, when executing the similar case search function,
the disease progress model building function is operated to first create
a plurality of models (temporal image feature amount change models) by
averaging successive time-series progress patterns of a disease. Then,
one of the created average disease progress models that best matches the
case to be inspected is selected. The selected average disease progress
model is applied to discrete time-series data unique to the case to be
inspected, thereby performing interpolation for generating successive
time-series data. A plurality of created models will be called "disease
progress models".

[0066] Details of the functions (case data generation function, disease
progress model building function, and similar case search function)
implemented by the similar case search program will be described.

2. Case Data Generation Function

[0067] Details of the case data generation function will be explained with
reference to the flowchart of FIG. 2. A case will be described, in which
time-series medical image data of the same patient are saved in
association with image feature amounts extracted from them, and medical
record data, as represented in Table 1.

[0068] In step S201, the CPU 101 reads out medical image data (to be
referred to as an image to be stored) to be stored in the case database
120 from the medical image database 130 in accordance with an input from
the mouse 112 or keyboard 113. The CPU 101 inputs the readout image to
the similar case search apparatus 100.

[0069] In the target image input processing, for example, the CPU 101
receives an image to be stored from the medical image database 130 via
the LAN 150, as described above. Alternatively, the CPU 101 reads out an
image to be stored from one of a storage device connected to the similar
case search apparatus 100 and various storage media such as an FDD, CD-RW
drive, MO drive, and ZIP drive.

[0070] In step S202, the CPU 101 selects medical record data corresponding
to the image to be stored from the medical record database 140. In the
medical record data selection processing, medical record data having the
same case data ID as a case data ID accessory to the image to be stored
is extracted from the medical record database 140.

[0071] In step S203, the CPU 101 extracts case data of the same patient as
that of the image to be stored from the case database 120. In the case
data extraction processing, case data having the same case data ID as a
case data ID accessory to the image to be stored are extracted from the
case database 120. A flag New is defined to represent whether the image
to be stored is new case data. When the case data are extracted from the
case database 120, New=F is set. If no case data is extracted, the image
to be stored is regarded as new case data, and the flag New=T is set.

[0072] In step S204, the CPU 101 recognizes a morbid region from the image
to be stored. In the morbid region recognition processing, the CPU 101
displays the image to be stored on the monitor 111 and stores a region
designated by an image interpreter in the main memory 102.

[0073] More specifically, the main memory 102 stores the number of a slice
(to be referred to as a slice of interest) which is designated by
operating the mouse 112 because the image interpreter determines that
this slice most properly visualizes a morbid region. The main memory 102
also stores the coordinates of a region of interest in the slice of
interest.

[0074] The monitor 111 displays past case data closest in imaging period
to the image to be stored among the case data extracted in step S203.
Further, the monitor 111 displays the region of interest of the morbid
portion. At this time, the image interpreter designates a region of
interest in the image to be stored that represents the same morbid
portion as the region of interest in the past case data. The main memory
102 stores the designated region of interest. As a result, the region of
interest of the same morbid portion as the past case data is extracted
from the image to be stored.

[0075] However, the processing of recognizing a morbid region from an
image to be stored is not limited to this. For example, the CAD technique
described in patent reference 1 may be adopted to automatically detect
the position where a morbid portion exists by analyzing an image to be
stored, and automatically set a region of interest containing the morbid
portion without the mediacy of an image interpreter.

[0076] This processing is executed only when the flag New=F is set in step
S203.

[0077] In step S205, the CPU 101 analyzes the image of the region of
interest recognized in step S204, extracting an image feature amount
representing the feature of the disease. For example, when the target
disease (abnormality) is pulmonary nodule, each element {f1,
f2, . . . , fn} (n is the element number of the image feature
amount) of an image feature amount vector F is the size of the nodule,
the contrast difference between the inside and periphery of the nodule,
the mean and variance of the internal density of the nodule, the
complexity of the boundary of the nodule region, or the like.

[0078] In step S206, the CPU 101 associates the image to be stored with
the medical record data extracted in step S202. Further, the CPU 101
associates the case data extracted in step S203, the morbid region
recognized in step S204, and the image feature amount extracted in step
S205.

[0079] In the association processing, the image to be stored, medical
record data, morbid region, and image feature amount are associated with
each other as one inspection data. If the flag New=F has been set in step
S203, the imaging period of the image to be stored is compared with that
of each image of the extracted case data. The inspection data including
the image to be stored are added to the case data so that they are
aligned in time series. Accordingly, medical image data are associated in
time series in the case data having the same ID.

[0080] To the contrary, if New=T has been set, the image to be stored is
new case data and is not associated with case data. The image to be
stored is stored as new case data in the case database 120.

[0081] By this processing, case data as represented in the case data table
of Table 1 are stored. Note that items such as the definite diagnosis
name and the progress of a disease in Table 1 are acquired from medical
record data.

3. Disease Progress Model Building Function

[0082] Details of the disease progress model building function will be
explained with reference to the flowchart of FIG. 3. The progress pattern
of a disease changes depending on the nature of a disease and the age
group and sex of a patient. Thus, the similar case search apparatus 100
according to the first embodiment defines different groups (to be
referred to as case groups) depending on the disease nature, patient's
age group, and patient's sex. A disease progress model is built for each
case group.

[0083] Table 2 exemplifies a case group defined for each disease nature,
patient's age group, and patient's sex.

[0084] When building a disease progress model for each case group,
respective case data stored in the case database 120 are classified into
the above-mentioned case groups. For each case group, a pattern is
attained by averaging the time-series progress patterns of respective
case data belonging to the group. The pattern is defined as a disease
progress model.

[0085] To obtain the average model of a plurality of case data,
time-series medical image data of the case data need to be made to
correspond to each other in the same coordinate system. In the first
embodiment, time-series medical image data of case data are made to
correspond to each other on the same temporal axis based on the stage of
a disease. The sequence of these processes will be explained with
reference to the flowchart of FIG. 3.

[0086] In step S301, the CPU 101 creates discrete time-series data of an
image feature amount from the image feature amounts of time-series
medical image data in each case data in the case database 120.

[0087] In this case, elements are made to correspond to each other between
a plurality of image feature amount vectors F in each case data. The
image feature amount vector of each medical image data in given case data
is defined as Fk={f1k, f2k, . . . ,
fnk}.

[0088] k is the number of the image feature amount vector of each medical
image data, and n is the number of the element of the image feature
amount vector. At this time, Fk elements are made to correspond to
each other between all medical image data contained in the same case
data. For example, for the first element of the image feature amount in
case data having a case data ID of 1 in Table 1, five vectors
f11, f12, f13, f14, and
f15 are made to correspond to each other. The remaining
elements are also similarly made to correspond to each other.

[0089] For each element after correspondence, a coordinate system is built
by plotting the time along the abscissa axis and the image feature amount
value along the ordinate axis. Image feature amounts plotted in this
coordinate system are defined as discrete time-series data of each
element.

[0090] FIG. 4 is a graph of discrete time-series data of the first element
of the image feature amount. The processing in step S301 is done for all
case data stored in the case database 120.

[0091] In step S302, the CPU 101 classifies discrete time-series data in
each case data that have been created in step S301, into one of the case
groups represented in Table 2.

[0092] For example, case data having "definite diagnosis name: diagnosis
name 1", "patient's sex: male", and "patient's age group: advanced age"
is classified into "case group ID: 3". The processing in step S302 is
performed for all case data stored in the case database 120.

[0093] In step S303, the CPU 101 sets the total number of case groups to N
and the target case group ID: i to 1.

[0094] In step S304, the CPU 101 makes all discrete time-series data
belonging to the target case group ID: i (to be referred to as group ID:
i) correspond to each other on the same coordinate axis. The processing
in step S304 will be exemplified below. However, the discrete time-series
data correspondence processing is not limited to the following method.

[0095] As shown in FIG. 4, if a diagnosis name is finalized at each time
point of discrete time-series data, the stage of the disease is also
finalized.

[0096] Based on the stage of the disease, discrete time-series data are
made to correspond to each other.

[0097] More specifically, DA and DB are two discrete time-series
data to each of which the stage of a disease is assigned on the time
axis. Each of PA and PB is a set of stages (five stages in this
example) of a disease assigned to a corresponding one of DA and
DB on the time axis. Note that there are as many PA and PB
values as diagnosed cases in the respective time-series data. At this
time, DB is translated to DA only along the time axis. The
translation stops at a position where the positions of PA and
PB values come closest to each other, thereby aligning DA and
DB.

[0098] Consequently, DA and DB are made to correspond to each
other to bring respective stages of diseases closest to each other. This
correspondence processing is executed between all discrete time-series
data belonging to the group ID: i.

[0099] In step S305, the CPU 101 creates a disease progress model based on
discrete time-series data belonging to the group ID: i that have been
made to correspond to each other on the same coordinate axis in step
S304.

[0100] More specifically, the CPU 101 creates a disease progress model
pattern by applying the least-squares method to all discrete time-series
data points plotted on the same coordinate axis. The disease progress
model is created for each element {f1, f2, . . . , fn} of
the image feature amount vector F. Further, a vector
Mi={M1i, M2i, . . . , Mni} of the
disease progress model having the group ID: i is defined.

[0101] FIG. 5 is a graph showing a disease progress model M1i
(curve indicated by a dotted line) obtained by applying the least-squares
method to discrete time-series data of the image feature amount fl
for four case data belonging to the group ID: i that are plotted on the
same coordinate axis.

[0102] In step S306, the CPU 101 saves the vector Mi of the disease
progress model obtained in step S305. Mi may be saved in the case
database 120 or magnetic disk 103.

[0103] In step S307, the CPU 101 increments the value of the group ID: i
by one.

[0104] In step S308, the CPU 101 checks the value of the group ID: i. If
the i value is equal to or smaller than the total number N of case
groups, the process returns to step S304; if NO, the disease progress
model building processing ends.

[0105] By this processing, the disease progress model of each case group
is built. After building the disease progress model of each case group,
the similar case search function can be executed.

4. Similar Case Search Function

[0106] Details of the similar case search function will be described with
reference to the flowchart of FIG. 6.

[0107] The first embodiment interpolates discrete time-series data by
applying a disease progress model generated by executing the disease
progress model building processing in FIG. 3 to discrete time-series data
of the image feature amount of a case to be inspected.

[0108] Then, the interpolated discrete time-series data is compared with
discrete time-series data of each case data in the case database 120,
calculating the similarity between them. With the interpolated discrete
time-series data, the similarity between the case to be inspected and
discrete time-series data at an arbitrary imaging time interval can be
calculated.

[0109] Case data similar to the case to be inspected are selected based on
the calculated similarity, and presented in time series. This processing
will be explained in detail with reference to the flowchart.

[0110] In step S601, the CPU 101 reads out an image to be inspected from
the medical image database 130 in accordance with an input from the mouse
112 or keyboard 113. The CPU 101 inputs the readout image to the similar
case search apparatus 100. The input processing is the same as step S201
of FIG. 2, and a detailed description thereof will not be repeated.

[0111] In step S602, the CPU 101 extracts case data of the same patient as
that of the image to be inspected from the case database 120, and
associates time-series medical image data contained in the extracted case
data. At this time, the case database 120 is assumed to have stored in
advance case data of the same patient as that of the image to be
inspected. The case data extraction processing in the association
processing is the same as the processes in steps S202 to S205 of FIG. 2,
so a detailed description thereof will not be repeated. During the
association processing, the morbid region of the image to be inspected is
recognized, and an image feature amount is extracted. The extracted case
data and the image to be inspected are associated with each other based
on the case data ID.

[0112] Table 3 represents the result of associating an image to be
inspected and extracted case data. As represented in Table 3, the
extracted case data contains morbid regions and image feature amounts in
time-series medical image data of the same patient imaged in the past.
Hence, these pieces of information on time-series medical image data of
the same patient as that of the image to be inspected are associated as a
case to be inspected.

[0113] In step S603, the CPU 101 creates discrete time-series data from
the image feature amounts in the time-series medical image data of the
case to be inspected that have been associated in step S602. More
specifically, an image feature amount Ft of the image to be
inspected in Table 2 is defined as Ft={fit, f2t,
. . . , fnt}. By the same processing as step S301 of FIG. 3,
Ft and the image feature amount vector Fk of each medical image
data in the extracted case data are made to correspond to each other for
each element.

[0114] FIG. 7 is a graph showing discrete time-series data of the first
image feature amount after the correspondence.

[0115] In step S604, the CPU 101 interpolates the discrete time-series
data to be inspected based on the disease progress model. The CPU 101
first classifies the case to be inspected into a case group in Table 2,
and then selects a disease progress model corresponding to the case
group. As a result, a disease progress model best matching the case to be
inspected is selected.

[0116] The disease progress model is selected from the case database 120
when stored in the case database 120, and from the magnetic disk 103 when
stored in the magnetic disk 103.

[0117] For example, when the case to be inspected belongs to "definite
diagnosis name: diagnosis name 1", "patient's sex: male", and "patient's
age group: advanced age", the vector M3 of a disease progress model
corresponding to "case group ID: 3" is selected. The selected disease
progress model is applied to discrete time-series data of the case to be
inspected.

[0118] FIG. 8 shows an example in which discrete time-series data
d1T of a case to be inspected for an image feature amount
f1 is interpolated by applying a disease progress model
M13 to d1T. Note that d1T: {f119,
f118, f117, f116, f1t}.

[0119] This processing is executed by the following procedures (but the
interpolation processing based on a disease progress model is not limited
to the following method).

[0120] First, the disease progress model M13 is divided at a
plurality of control points and approximated by a spline curve C1.
In this case, a division interval α is set to 1/10 of the entire
time of discrete time-series data.

[0121] Then, the discrete time-series data d1T of a case to be
inspected and the curve C1 are aligned by the same processing as
step S304 of FIG. 3.

[0122] Several control points on the curve C1 are replaced with
points on d1T to redraw a spline curve C1t. For
example, control points falling within a predetermined distance (α
is applied in this example) from each point f1k contained in
d1T are replaced with points f1k.

[0123] Based on an average disease progress model, unique successive
time-series data containing the discrete time-series data d1T
are created. These data are called interpolated time-series data of the
case to be inspected for the image feature amount f1k, and
represented by data q1T. The same processing is also performed
for the remaining image feature amounts f2, . . . , fn,
creating an interpolated time-series data vector QT={q1T,
q2T, . . . , qnT}.

[0124] In step S605, the CPU 101 calculates the similarity between the
interpolated time-series data of the case to be inspected that have been
created in step S604 and discrete time-series data of each case data
stored in the case database 120.

[0125] In the similarity calculation processing, similar to step S304 of
FIG. 3, the interpolated time-series data of the case to be inspected and
discrete time-series data of the case data ID: i to be compared are
aligned on the same time axis so that respective stages of diseases in
these data come closest to each other.

[0126] The vector of discrete time-series data of the case data ID: i will
be called Di={d1i, d2i, . . . , dni}.

[0127] A method of calculating similarity Si between data QT and
data Di will be exemplified. However, the method of calculating the
similarity Si is not limited to the following one.

[0128] The data QT is successive time-series data and contains data
corresponding to respective time points of the discrete time-series data
Di on the time axis. Discrete time-series data obtained by plotting
data at the same time points as discrete time points in the data Di
are extracted and defined as Q'T={q'1T, q'2T, .
. . , q'nT}.

[0129] The data Q'T and data Di have data at the same discrete
time points. By comparing the image feature amounts of the data Q'T
and data Di at respective time points, image feature amounts having
no time difference can be compared.

[0130] Discrete time points of the data Di are defined as p1,
p2, . . . , pm (m is the sum of discrete points). FT,pj
and Fi,pj are the image feature amount vectors of Q'T and Di at
a given time point pj (1≦j≦m). Equation (1) is an
example of an equation for calculating similarity si,pj between
FT,pj) and Fi,pj:

[0131] Equation (2) is an example of an equation for calculating
similarity Si in time series:

[ Mathematical 2 ] s i = j = 1 m s i
, pj m ( 2 ) ##EQU00002##

[0132] In this case, the average value of the similarity si,pj at
each time point is set as the similarity Si in time series.
Similarities between the remaining case data stored in the case database
120 and the case to be inspected are also calculated by the same method.

[0133] In step S606, the CPU 101 selects case data similar to the case to
be inspected, based on the similarity calculated in step S605. In the
first embodiment, a plurality of case data (constant b) are selected in
descending order of similarity with the case to be inspected. For
example, b=5 is applied, and five case data are selected.

[0134] In step S607, the CPU 101 presents, in time series, the case data
(similar case data) which are similar to the case to be inspected and
have been selected in step S606.

[0135] FIGS. 9A and 9B are views exemplifying a similar case data
presentation method in the first embodiment. As shown in FIGS. 9A and 9B,
the abscissa represents the time axis. Time-series images of interest of
a morbid portion in a case to be inspected are arranged above the time
axis. At this time, the imaging date and time, the diagnosis name, and
the stage of a disease are also arranged. Similar case data aligned in
step S605 are displayed in the same way in descending order of
similarity. If a medical treatment has been done for selected similar
case data, the medical treatment method is also displayed.

[0136] As is apparent from the above description, the similar case search
apparatus according to the first embodiment calculates similarity using
interpolated time-series data obtained by interpolating the discrete
values of time-series image feature amounts.

[0137] The similar case search apparatus can present even similar case
data which is different in imaging time interval and inspection time
interval from a case to be inspected but exhibits a similar progress.

[0138] The similar case search apparatus according to the first embodiment
also presents an inspection and medical treatment given to a patient
corresponding to similar case data. The similar case search apparatus can
provide not only information useful for diagnosing a case to be inspected
but also information helpful for policymaking by an image interpreter
when examining an inspection policy and treatment policy.

Modification 1 to First Embodiment

[0139] The disease progress model building processing described with
reference to FIG. 3 may be executed based on statistical data when the
progress pattern of a disease, which changes depending on, for example,
the nature of a disease and the sex and age of a patient, has been known
statistically.

Modification 2 to First Embodiment

[0140] In the similar case data selection processing in step S606 of FIG.
6, similar case data is presented based on the similarity of an image
feature amount change pattern. Instead, similar case data may be
presented based on, for example, comparison with another clinical
information.

[0141] Information on the sex and age of a patient is considered when
classifying a case to be inspected and case data in the case database 120
into case groups. As other kinds of clinical information, the past
history, smoking history, and genetic information of a patient may be
taken into account when narrowing similar case data.

[0142] The past history and smoking history of a patient are likely to be
correlated to a definite diagnosis name, and genetic information is
likely to be correlated to the effects of a drug. Selected case data are
narrowed down to case data having these kinds of information matching
those of a case to be inspected, thereby increasing the similar case data
search precision.

Modification 3 to First Embodiment

[0143] In the processing of presenting similar case data in time series in
step S607 of FIG. 6, for example, at least one image feature amount
highly correlated to a change of the stage of a disease may be selected
to present discrete time-series data of the image feature amount.

[0144] For example, when a disease to be inspected is pulmonary nodule, a
change of the stage of the disease is highly correlated to a change of
the size of the pulmonary nodule. Thus, discrete time-series data
representing a change of the size of the pulmonary nodule in similar case
data are presented. An image interpreter can predict a future change of
the size of the pulmonary nodule in the case to be inspected.

Second Embodiment

[0145] In the first embodiment, disease progress models are classified by
the disease nature, patient's age group, and patient's sex. However, the
present invention is not limited to this.

[0146] Generally when a disease is treated, it follows a different
progress pattern depending on the treatment. In the second embodiment, a
case group (to be referred to as a prognosis case group) after the start
of a treatment (to be referred to as prognosis) is newly defined. A
corresponding progress pattern model (to be referred to as a treatment
progress model) is built.

[0147] The overall configuration of a medical information system and the
arrangement of a similar case search apparatus in the second embodiment
are the same as those in the first embodiment, and a description thereof
will not be repeated. A disease progress model before the start of a
treatment (to be referred to as pretreatment) is built by the same method
as that in the first embodiments, and a description thereof will not be
repeated.

[0148] Table 4 exemplifies a prognosis case group. The progress of a
disease after treatment depends on that of a disease before treatment.
The prognosis case group is therefore defined by subdividing the
pretreatment case group. For example, a pretreatment case group ID: 1 is
subdivided into prognosis case group IDs: 1 to 6.

[0149] For "non-execution of an operation", the state of a disease changes
continuously depending on medication. Thus, the treatment progress model
is built based on the flowchart of FIG. 3, as well as a disease progress
model before treatment.

[0150] For "execution of an operation", the state of a disease before an
operation is the same as that for "non-execution of an operation". After
the operation, a morbid portion is resected and the state of the disease
greatly changes. Hence, for "execution of an operation", a treatment
progress model (to be referred to as a postoperative treatment progress
model) is built based on postoperative discrete time-series data of case
data. A treatment progress model for "non-execution of an operation" will
be called a preoperative treatment progress model to discriminate it from
the postoperative treatment progress model.

[0151] In the second embodiment, case data stored in a case database 120
are case data obtained by adding an item regarding a treatment situation
(one of pretreatment, start of treatment, and prognosis) to case data
described in the first embodiment.

[0152] Table 5 exemplifies a case data table stored in the case database
120 in the second embodiment.

[0153] The sequence of similar case search processing in a similar case
search apparatus 100 according to the second embodiment will be explained
with reference to the flowchart of FIG. 10.

[0154] In the second embodiment, discrete time-series data of a case to be
inspected are divided into two, pretreatment data and prognosis data. The
divided discrete time-series data are interpolated based on a disease
progress model and treatment progress model, respectively. For each of
the pretreatment data and prognosis data, the similarity between the
interpolated data and case data in the case database 120 is calculated
(first and second calculation units).

[0155] For each case data, total similarity is calculated based on both
the similarities of the pretreatment data and prognosis data. Based on
the calculated similarity, case data similar to the case to be inspected
are selected and presented in time series. As for steps of performing the
same processes as those in the flowchart of FIG. 6 in the first
embodiment, only correspondences with the steps in FIG. 6 will be
described and a detailed description thereof will not be repeated.

[0156] Processes in steps S1001 to S1003 are the same as those in steps
S601 to S603 of FIG. 6. Processes in steps S1005 and S1006 are the same
as those in steps S604 and S605.

[0157] In step S1004, a CPU 101 divides, into pretreatment data and
prognosis data, discrete time-series data of the image feature amount of
each case data that have been created in step S1003.

[0158] In step S1007, the CPU 101 interpolates, based on the
above-mentioned treatment progress model, discrete time-series data (to
be referred to as prognosis discrete time-series data) of the prognosis
to be inspected.

[0159] More specifically, similar to step S604 of FIG. 6, the CPU 101
classifies the case to be inspected into a prognosis case group in Table
4, and selects a treatment progress model corresponding to the prognosis
case group.

[0160] At this time, when the case to be inspected corresponds to
"execution of an operation" and medication was done before the operation,
a corresponding postoperative treatment progress model and a preoperative
treatment progress model for the same medication are selected.

[0161] After that, the treatment progress model is applied to the discrete
time-series data of the case to be inspected by the same processing as
step S604. When both the preoperative and postoperative treatment
progress models are selected, they are applied to preoperative prognosis
discrete time-series data and postoperative prognosis discrete
time-series data.

[0162] In step S1008, the CPU 101 calculates similarity Spost between
interpolated prognosis time-series data of the case to be inspected that
has been created in step S1007, and discrete time-series data of each
case data stored in the case database 120.

[0163] More specifically, the same processing as step S605 of FIG. 6 is
done. When the interpolated prognosis time-series data are generated
based on the two, preoperative and postoperative treatment progress
models, the interpolated time-series data are divided into interpolated
preoperative time-series data and interpolated postoperative time-series
data. After the similarities of the respective interpolated time-series
data are calculated, a value obtained by weighting and averaging these
similarities according to equation (3) is defined as Spost:

[Mathematical 3]

Spost=uS1+(1-u)S2 (3)

[0164] where S1 is the similarity of preoperative discrete
time-series data, S2 is the similarity of postoperative discrete
time-series data, and u is a constant of a real number. In this case, u
represents the ratio at which the time interval of preoperative prognosis
discrete time-series data occupies prognosis time-series data of a case
to be inspected.

[0165] In step S1009, the CPU 101 selects case data similar to the case to
be inspected based on the pretreatment and prognosis similarities
calculated in steps S1006 and S1008, respectively.

[0166] In the second embodiment, a value obtained by weighting and
averaging the pretreatment similarity (to be referred to as Spre)
and the prognosis similarity Sport is calculated as the total
similarity of the same case data according to equation (4):

[Mathematical 4]

Sall=vSpre+(1-v)Spost (4)

[0167] where v is a constant of a real number. In this case, v represents
the ratio at which the time interval of pretreatment time-series data
occupies all discrete time-series data of a case to be inspected.

[0168] Similar to step S606 of FIG. 6, the CPU 101 selects a plurality of
similar case data in descending order of the total similarity with the
case to be inspected.

[0169] In step S1010, the CPU 101 presents, in time series, the similar
case data selected in step S1009.

[0170] FIGS. 11A and 11B are views exemplifying a similar case data
presentation method in the second embodiment. The display form in FIGS.
11A and 11B is the same as that in FIGS. 9A and 9B in the first
embodiment. However, the display form in FIGS. 11A and 11B presents cases
which are similar to a case to be inspected, which has already undergone
a treatment, in the progress of a disease before the treatment and the
progress of mediation before and after an operation (first and second
output units).

[0171] As is apparent from the above description, the similar case search
apparatus according to the second embodiment discriminates data before
and after a treatment and before and after an operation, which change the
progress of a disease. The similar case search apparatus interpolates
discrete time-series data based on different disease progress models and
separately calculates similarities.

[0172] For example, when discrete time-series data is interpolated based
on only a pretreatment disease progress model for a case to be inspected
which has already undergone a treatment, a mismatch occurs between the
discrete time-series data and the pretreatment disease progress model.
However, the similar case search apparatus according to the second
embodiment can solve this problem.

[0173] If the pretreatment progress pattern of a case to be inspected is
accidentally close to the prognosis progress pattern of case data stored
in the case database 120, the case data is presented as one with high
similarity though it is not similar case data originally. However, the
similar case search apparatus according to the second embodiment can
solve this problem.

[0174] Resultantly, the similar case search apparatus according to the
second embodiment can more accurately interpolate discrete time-series
data and calculate similarity.

Modification 1 to Second Embodiment

[0175] In step S1009, a specific number of similar case data are selected
in descending order of total similarity calculated from pretreatment and
prognosis similarities. However, the present invention is not limited to
this. For example, a specific number of case data may be selected in
descending order of each of pretreatment and prognosis similarities.

[0176] This method can present similar cases which pay attention to
pretreatment and prognosis. This method is effective when an image
interpreter wants to refer to a plurality of case data similar in only
the progress of a disease before treatment so that he can confirm how the
prognosis progresses of these case data differ from each other, and vice
versa.

[0177] Further, the preoperative and postoperative similarities of
prognosis data may be calculated separately to present similar case data
which pay attention to pretreatment, pre-operation, and post-operation.
Accordingly, this method can present similar case data similar in only
process after an operation as a reference for treatment policymaking.

Modification 2 to Second Embodiment

[0178] In the second embodiment, when building a treatment progress model,
case groups are created in accordance with treatment methods
"execution/non-execution of an operation" and "administered drug", as
represented in Table 4. However, treatment methods for classification
into case groups are not limited to them.

[0179] Another treatment method which may affect the progress pattern of a
disease is, for example, "radiotherapy". Depending on radiotherapy, case
groups may be created. When presenting similar case data, information on
"radiotherapy" may be presented as treatment information.

Third Embodiment

[0180] In the first and second embodiments, similarity is calculated by
comparing interpolated time-series data obtained by interpolating
discrete time-series data of a case to be inspected with discrete
time-series data of case data in the case database 120. However, the
present invention is not limited to this.

[0181] In contrast to the first and second embodiments, similarity may be
calculated by comparing discrete time-series data of a case to be
inspected with interpolated time-series data obtained by interpolating
discrete time-series data of case data in a case database 120.

[0182] In this arrangement, discrete time-series data of case data in the
case database 120 is interpolated in advance, and stored in a magnetic
disk 103 easily accessible by a CPU 101 in similar case search. In
similar case search, interpolated time-series data of each case data
stored in the magnetic disk 103 is read out for comparison with discrete
time-series data of a case to be inspected.

[0183] In this arrangement, interpolated time-series data of case data in
the case database 120 is stored in advance in the magnetic disk 103. When
executing similar case search, no interpolated time-series data need be
created.

[0184] This can obviate the need to interpolate discrete time-series data
of a case to be inspected in similar case search. The third embodiment
can shorten the time of similar case search processing, as compared with
the first and second embodiments.

Other Embodiments

[0185] The present invention is also achieved by executing the following
processing. More specifically, software (program) for implementing the
functions of the above-described embodiments is supplied to a system or
apparatus via a network or various storage media. The computer (or the
CPU or MPU) of the system or apparatus reads out and executes the
program.

[0186] While the present invention has been described with reference to
exemplary embodiments, it is to be understood that the invention is not
limited to the disclosed exemplary embodiments. The scope of the
following claims is to be accorded the broadest interpretation so as to
encompass all such modifications and equivalent structures and functions.

[0187] This application claims the benefit of Japanese Patent Application
No. 2009-006117, filed Jan. 14, 2009, which is hereby incorporated by
reference herein in its entirety.